Nowcasting economic activity in Argentina with many predictors

Autores
D'Amato, Laura; Garegnani, María Lorena; Blanco, Emilio
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.
Facultad de Ciencias Económicas
Materia
Ciencias Económicas
Forecast pooling
Large dataset
Real time forecast
Factor Models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/170412
Acceder
id SEDICI_d0d39fa7b36226677c6da542660f30fc
oai_identifier_str oai:sedici.unlp.edu.ar:10915/170412
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Nowcasting economic activity in Argentina with many predictorsD'Amato, LauraGaregnani, María LorenaBlanco, EmilioCiencias EconómicasForecast poolingLarge datasetReal time forecastFactor ModelsWe pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.Facultad de Ciencias Económicas2011-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/170412enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-99570-9-7info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2011/Damato.pdfinfo:eu-repo/semantics/altIdentifier/issn/1852-0022info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:24:15Zoai:sedici.unlp.edu.ar:10915/170412Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:24:15.865SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Nowcasting economic activity in Argentina with many predictors
title Nowcasting economic activity in Argentina with many predictors
spellingShingle Nowcasting economic activity in Argentina with many predictors
D'Amato, Laura
Ciencias Económicas
Forecast pooling
Large dataset
Real time forecast
Factor Models
title_short Nowcasting economic activity in Argentina with many predictors
title_full Nowcasting economic activity in Argentina with many predictors
title_fullStr Nowcasting economic activity in Argentina with many predictors
title_full_unstemmed Nowcasting economic activity in Argentina with many predictors
title_sort Nowcasting economic activity in Argentina with many predictors
dc.creator.none.fl_str_mv D'Amato, Laura
Garegnani, María Lorena
Blanco, Emilio
author D'Amato, Laura
author_facet D'Amato, Laura
Garegnani, María Lorena
Blanco, Emilio
author_role author
author2 Garegnani, María Lorena
Blanco, Emilio
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Económicas
Forecast pooling
Large dataset
Real time forecast
Factor Models
topic Ciencias Económicas
Forecast pooling
Large dataset
Real time forecast
Factor Models
dc.description.none.fl_txt_mv We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.
Facultad de Ciencias Económicas
description We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.
publishDate 2011
dc.date.none.fl_str_mv 2011-11
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/170412
url http://sedici.unlp.edu.ar/handle/10915/170412
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-99570-9-7
info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2011/Damato.pdf
info:eu-repo/semantics/altIdentifier/issn/1852-0022
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846783695574269952
score 12.982451

Publicaciones similares